Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering
Abstract
:1. Introduction
- (1)
- We develop an error propagation model of the Kalman filter that takes different sources of errors (quantization, unreliable memories) into account and allows us to derive new filter equations to minimize the estimation error. Moreover, these equations accurately predict the filter’s performance, depending on the considered sources of errors and on their parameters.
- (2)
- We propose a methodology for minimizing the energy of the unreliable memories used in the Kalman filter, under a given performance constraint. This methodology consists of computing the optimal number of quantization levels and bit energy allocation in two setups. The first setup considers that the B energy levels can be chosen freely, while the second one assumes that only energy levels can be set.
2. System Model
2.1. Kalman Filter
2.2. Quantized Implementation of the Filter
2.3. Implementation of the Filter by Using an Unreliable Memory
3. Error Analysis
3.1. Error Propagation Model
3.2. Quantization Error
3.3. Unreliable Memory Error
3.4. Total Error
4. Energy Optimization
4.1. Optimization across All the Bits
Algorithm 1: Computing the optimal values for and m. |
Input: , a, , , |
Result: Optimal number of bits and optimal energy allocation vector |
4.2. Optimization with a Limited Number of Energy Levels
5. Simulation Results
5.1. Accuracy of the Theoretical Analysis
5.2. Solutions to the Optimization Problems
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A. Computation of
Appendix B. Computation of the Optimal Solution to Problem 1
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Kern, J.; Dupraz, E.; Aïssa-El-Bey, A.; Varshney, L.R.; Leduc-Primeau, F. Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering. Sensors 2022, 22, 853. https://doi.org/10.3390/s22030853
Kern J, Dupraz E, Aïssa-El-Bey A, Varshney LR, Leduc-Primeau F. Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering. Sensors. 2022; 22(3):853. https://doi.org/10.3390/s22030853
Chicago/Turabian StyleKern, Jonathan, Elsa Dupraz, Abdeldjalil Aïssa-El-Bey, Lav R. Varshney, and François Leduc-Primeau. 2022. "Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering" Sensors 22, no. 3: 853. https://doi.org/10.3390/s22030853
APA StyleKern, J., Dupraz, E., Aïssa-El-Bey, A., Varshney, L. R., & Leduc-Primeau, F. (2022). Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering. Sensors, 22(3), 853. https://doi.org/10.3390/s22030853